Detection of rotated objects using the improved YOLOv5 algorithm

Wudi Tang; Xuan Huang; Hu Wei; Dong Li

doi:10.1117/12.2628757

7 March 2022 Detection of rotated objects using the improved YOLOv5 algorithm

Wudi Tang, Xuan Huang, Hu Wei, Dong Li

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672H (2022) https://doi.org/10.1117/12.2628757
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

Common object detection algorithms have too many backgrounds, duplicated boxes, and a high miss ratio in detecting rotated objects, which limit their applications on the industrial site. To address these problems, this paper proposed an improved YOLOv5 to detect rotated objects. First, this paper used the K-means clustering algorithm to develop clustering analysis for trained datasets to confirm more proper anchor boxes to reduce the training time. Then this paper transferred the angle issue to a classification problem. This paper also learned angles in the original loss function combined with the circular smooth label (CSL) algorithm, thus avoiding the periodicity of angle regression. Last, this paper selected one from different detection results of an object to increase the accuracy of the detection results. The experiment showed that the proposed algorithm had a higher detection precision than other methods in the public dataset DOTA. When the proposed algorithm detected rotated objects in the dataset collected on the industrial site, its mAP reached 94.35%. This value was 8.20% higher than that of YOLOv5, satisfying the detection requirement on the industrial site.

Citation Download Citation

Wudi Tang, Xuan Huang, Hu Wei, and Dong Li "Detection of rotated objects using the improved YOLOv5 algorithm", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672H (7 March 2022); https://doi.org/10.1117/12.2628757

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Detection and tracking algorithms

Algorithm development

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